Standard escape codes are prefixed with Escape:
- Ctrl-Key:
^[ - Octal:
\033 - Unicode:
\u001b - Hexadecimal:
\x1B - Decimal:
27
As a security professional, it is important to conduct a thorough reconnaissance. With the increasing use of APIs nowadays, it has become paramount to keep access tokens and other API-related secrets secure in order to prevent leaks. However, despite technological advances, human error remains a factor, and many developers still unknowingly hardcode their API secrets into source code and commit them to public repositories. GitHub, being a widely popular platform for public code repositories, may inadvertently host such leaked secrets. To help identify these vulnerabilities, I have created a comprehensive search list using powerful search syntax that enables the search of thousands of leaked keys and secrets in a single search.
(path:*.{File_extension1} OR path:*.{File_extension-N}) AND ({Keyname1} OR {Keyname-N}) AND (({Signature/pattern1} OR {Signature/pattern-N}) AND ({PlatformTag1} OR {PlatformTag-N}))
**1.
The GMStorage class is an advanced wrapper for Greasemonkey, Tampermonkey, or Violentmonkey storage APIs. It streamlines persistent data management by offering a natural JavaScript object interface with built-in synchronization across tabs, deep object update detection, and robust merging of default values.
Key Features:
Natural Read/Write Operations:
Access and update storage keys as if they were object properties.
Deep Proxying for Nested Updates:
package.json, etc.) for available scripts.This repository contains a disciplined, evidence-first prompting framework designed to elevate an Agentic AI from a simple command executor to an Autonomous Principal Engineer.
The philosophy is simple: Autonomy through discipline. Trust through verification.
This framework is not just a collection of prompts; it is a complete operational system for managing AI agents. It enforces a rigorous workflow of reconnaissance, planning, safe execution, and self-improvement, ensuring every action the agent takes is deliberate, verifiable, and aligned with senior engineering best practices.
I also have Claude Code prompting for your reference: https://gist.github.com/aashari/1c38e8c7766b5ba81c3a0d4d124a2f58
| description |
|---|
Critical analysis of problems with root cause identification before proposing solutions |
Thoroughly analyze the current problem before proposing any solutions. Focus on identifying root causes and asking clarifying questions.
| /* | |
| * Copyright 2025 Kyriakos Georgiopoulos | |
| * | |
| * Licensed under the Apache License, Version 2.0 (the "License"); | |
| * you may not use this file except in compliance with the License. | |
| * You may obtain a copy of the License at | |
| * | |
| * http://www.apache.org/licenses/LICENSE-2.0 | |
| * |
In a recent blog post, Ben Recht described the Reinforcement Learning (RL) setup as:
Paraphrasing Thorndike’s Law of Effect, Lior defines reinforcement learning as the iterative process:
- Receive external validation on how good you’re currently doing
- Adjust what you’re currently doing so that you are better the next time around.
Whether or not this is how humans or animals learn, this is a spot-on definition of computer scientific reinforcement learning.
This document describes the workflow for an orchestrator agent to break down a large task into sub-tasks, delegate to worker agents, and coordinate the work to completion.
┌─────────────────────────────────────────────────────────────────┐
│ Orchestrator Agent │
│ │